Tumour Stroma Ratio Assessment Using Digital Image Analysis Predicts Survival in Triple Negative and Luminal Breast Cancer

Ewan Ka Millar,Jodi Lynch,Kirsty Lee,Julia Beretov,Lois H Browne,Alexander Swarbrick,Peter H Graham

doi:10.3390/cancers12123749

Abstract

Simple SummaryTumour stroma is known to predict outcome and play an important role in the growth and spread of solid tumours and their response to therapy. In breast cancer, there is evidence that the tumour stroma ratio (TSR) can predict outcome in aggressive triple negative breast cancer, but its value for the more common hormone receptor positive breast cancer is unclear. Using computerised image analysis and machine learning algorithms, we show that TSR is an important factor in predicting outcome for triple negative disease and hormone receptor positive cancer. However, its influence on good or poor outcome appears to depend on tumour type and the relative predominance of the stromal component. By better understanding the role of the tumour stroma in cancer growth, and its response to treatment, this study may help support the role of TSR as a new prognostic marker for breast cancer to guide clinical decision making.We aimed to determine the clinical significance of tumour stroma ratio (TSR) in luminal and triple negative breast cancer (TNBC) using digital image analysis and machine learning algorithms. Automated image analysis using QuPath software was applied to a cohort of 647 breast cancer patients (403 luminal and 244 TNBC) using digital H&E images of tissue microarrays (TMAs). Kaplan–Meier and Cox proportional hazards were used to ascertain relationships with overall survival (OS) and breast cancer specific survival (BCSS). For TNBC, low TSR (high stroma) was associated with poor prognosis for both OS (HR 1.9, CI 1.1–3.3, p = 0.021) and BCSS (HR 2.6, HR 1.3–5.4, p = 0.007) in multivariate models, independent of age, size, grade, sTILs, lymph nodal status and chemotherapy. However, for luminal tumours, low TSR (high stroma) was associated with a favourable prognosis in MVA for OS (HR 0.6, CI 0.4–0.8, p = 0.001) but not for BCSS. TSR is a prognostic factor of most significance in TNBC, but also in luminal breast cancer, and can be reliably assessed using quantitative image analysis of TMAs. Further investigation into the contribution of tumour subtype stromal phenotype may further refine these findings.

Highlights

Breast cancer is the most common cancer in women and the most common cause of cancer deaths worldwide with an estimated 2.1 million cases diagnosed and more than 620,000 deaths globally in2018 [1]
In breast cancer several studies have demonstrated an association of poor outcome with high stroma in triple negative breast cancer (TNBC), but there have been some conflicting results for estrogen receptor (ER)+ disease
Our study offers an alternative methodology using tissue microarrays (TMAs) and automated image analysis and machine learning algorithms, which provides rapid, objective, quantitative area estimation, suitable for application to large clinical trial cohorts with efficient rapid throughput of data

Summary

Introduction

Breast cancer is the most common cancer in women and the most common cause of cancer deaths worldwide with an estimated 2.1 million cases diagnosed and more than 620,000 deaths globally in2018 [1]. The tumour stroma contains cancer-associated fibroblasts (CAFs), known to be involved in cellular crosstalk, the induction of local immunosuppression and resistance to chemotherapy in triple negative breast cancer (TNBC) and endocrine therapy in ER+ breast cancer [4,5,6,7]. In breast cancer several studies have demonstrated an association of poor outcome with high stroma in TNBC, but there have been some conflicting results for ER+ disease (reviewed in detail in [9]). There is increasing evidence to suggest that CAFs may provide a specialised niche for cancer stem cells and influence responsiveness to chemotherapy in TNBC [5,15,16] and endocrine therapy in ER+ disease [17,18], supported by more recent single cell sequencing studies identifying specific subtypes of CAFs in mouse and human breast tumours [14,19]

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